Dietary intake, energy availability, and power in men collegiate gymnasts.

Introduction: The purpose was to examine the prevalence of low energy availability (LEA), explore dietary behaviors in men collegiate gymnasts (n = 14), and investigate the relationships between energy availability (EA), body composition, and plyometric performance. Methods: Body composition was measured using air displacement plethysmography. Lower- and upper-body peak power (PWRpeak) and modified reactive strength index (RSImod) were calculated from countermovement jump (CMJ) and plyometric push-up (PP) assessments. Energy expenditure was tracked over 3 days, while daily energy and macronutrient intake were recorded. EA was calculated and used to categorize athletes into LEA and non-LEA groups. Pearson correlation coefficients were used to examine relationships between EA, body composition, and performance metrics. Results: 85.7% of athletes (n = 12) exhibited LEA (20.98 ± 5.2 kcals/kg FFM), with non-LEA athletes (n = 2) marginally surpassing the <30 kcal/kg of fat-free mass (FFM) threshold (30.58 ± 0.2 kcals/kg FFM). The cohort (n = 14) consumed insufficient energy (30.5 ± 4.5 kcal/kg/day) and carbohydrates (3.7 ± 1.1 g/kg/day), resulting in LEA (22.36 ± 5.9 kcal/kg/FFM). EA was not correlated with body composition or performance metrics. Discussion: A high prevalence of LEA may exist in men gymnasts, largely due to a low relative energy and carbohydrate intake.

Distributed Wearable Ultrasound Sensors Predict Isometric Ground Reaction Force.

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N = 5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US: RF, and SMART-US: VMO models (p < 0.05). Model validation showed that the Distributed SMART-US model had an R2 of 0.80 ± 0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation.

Fat-Free Mass Index in Sport: Normative Profiles and Applications for Collegiate Athletes.

ABSTRACT: Jagim, AR, Harty, PS, Jones, MT, Fields, JB, Magee, M, Smith-Ryan, AE, Luedke, J, and Kerksick, CM. Fat-free mass index in sport: normative profiles and applications for collegiate athletes. J Strength Cond Res 38(9): 1687-1693, 2024-Recent concerns have been raised regarding the ethical considerations of conducting body composition assessments in sports. Specific apprehensions pertain to the inappropriate use of percent body fat and the limited application of the results to performance and recovery. Fat-free mass index (FFMI), a height-adjusted assessment of FFM, can serve as an alternative body composition metric to focus on in sports. Fat-free mass index provides valuable context regarding an optimal amount of FFM, accounting for skeletal frame and height while helping to qualify an athlete's FFM as low, moderate, or high. This review posits that shifting the focus of body composition measures to FFM can support more ideal targets across athletic seasons, careers, and the return to play after injury. In addition, a FFM focus may help change the perception of body composition assessment and how athletes perceive their current body because of the increased focus on the FFM compartment and the goal of maximizing tissue accrual, rather than focusing on fat loss. Fat-free mass index is calculated by dividing FFM (kg) by height (m 2 ) and can serve to normalize FFM, relative to height, and enable comparisons across athletes, sport types, and sex. Previous research has identified differences in FFMI across sex and among sport categories. Still, there is a need for more published data to develop optimal ranges for FFMI across sex, sports, and positions. As more data become available, FFMI has the potential to provide normative guidelines for optimal FFM development, performance, and injury risk reduction. The purpose of the current review was to summarize FFMI values across collegiate sport categories and competitive status to provide normative profiles, according to sex and sport type.

Fat-Free Mass Index in a Large Sample of Collegiate American Football Athletes.

High levels of fat-free mass (FFM) are favorable for athletes and are related to sport performance. However, fat-free mass index (FFMI), which includes adjustments for height, may offer a better way to characterize FFM beyond raw values. As FFMI is understudied relative to sport, the purpose of the current study was to assess position and age group differences in FFMI among collegiate American football players. National Collegiate Athletic Association DIII (n=111) football players underwent body composition assessment via bioelectrical impedance analysis. FFMI was calculated by dividing FFM by height squared. One-way analyses of variance with Bonferroni post-hoc tests were conducted to evaluate differences in FFMI by position and age groups (α<0.05). The overall mean FFMI was 23.50 ± 2.04 kg · m-2, with values ranging from 18.1-27.7 kg · m-2. FFMI was highest in linemen (24.8 ± 1.5 kg · m-2) and lowest in specialty players (20.6 ± 1.4 kg · m-2) (p<0.05). No differences in FFMI were apparent across age groups (p>0.05). Current findings demonstrate that an athlete's upper limit for FFMI may exceed 25 kg · m-2, and differences exist across positions, likely due to position-specific demands. These measurements serve as a foundation for tailoring nutritional and exercise plans, forecasting athletic performance, and supplying coaches with standardized data about the potential for additional FFM accretion in collegiate American football players.

Fat-Free Mass Index in a Large Sample of National Collegiate Athletic Association Men and Women Athletes From a Variety of Sports.

ABSTRACT: Magee, MK, Fields, JB, Jagim, AR, and Jones, MT. Fat-free mass index in a large sample of National Collegiate Athletic Association men and women athletes from a variety of sports. J Strength Cond Res 38(2): 311-317, 2024-Fat-free mass index (FFMI) can be used to categorize fat-free mass (FFM) relative to height. Normative values have been established in a variety of sports, as has suggested lower and upper thresholds of FFMI. However, FFMI has not been reported in a large sample of athletes, representing both sexes and multiple sport types. The purpose of this study was to evaluate differences in FFMI and establish normative values across a large sample of collegiate sports. A total of 1,961 athletes (men: n = 596, 10 sports; women: n = 1,365, 8 sports) participated. Height and mass were measured using a stadiometer and calibrated digital scale, respectively. Fat-free mass was assessed with air displacement plethysmography and used to calculate FFMI. Kruskal-Wallis test and one-way analysis of variance evaluated differences in FFMI in women's and men's sports, respectively. When collapsed across sport type and stratified by sex, men had a higher FFMI (21.5 ± 1.9 kg·m -2 vs. 17.9 ± 1.8 kg·m -2 ; p < 0.001). Differences occurred in FFMI across sport. In women, basketball athletes had the highest FFMI (18.9 kg·m -2 ) and highest 99th percentile for FFMI, whereas rowers had the lowest (16.9 kg·m -2 ). In men, throwers had the highest FFMI (25.7 kg·m -2 ) and highest 99th percentile, whereas volleyball athletes had the lowest (19.9 kg·m -2 ). Differences in FFMI may be attributable to dietary habits and physiological demands of respective sports. These results can provide information relative to differences in FFMI values across sports. In addition, sport-specific normative values can be used as benchmarks and targets for training, nutrition, and goal setting.

Body Composition, Energy Availability, Risk of Eating Disorder, and Sport Nutrition Knowledge in Young Athletes.

Young athletes may be at risk for low energy availability (LEA) or dietary habits that are indicative of eating disorders. Thus, the purpose of the current study was to investigate the prevalence of LEA among high school athletes and examine those at risk for eating disorders. A secondary aim was to examine relationships between sport nutrition knowledge, body composition, and LEA. METHODS: 94 male (n = 42) and female (n = 52) mean ± SD age: 18.09 ± 2.44 y; height: 172.6 ± 9.8 cm; body mass: 68.7 ± 14.5 kg; BMI: 22.91 ± 3.3 kg·m-2) athletes completed a body composition assessment and electronic versions of the abridged sports nutrition knowledge questionnaire (ASNK-Q), brief eating disorder in athletes questionnaire (BEDA-Q), and the low energy availability for females questionnaire (LEAF-Q; females only). RESULTS: 52.1% of female athletes were classified as being at risk for LEA. Moderate inverse relationships existed for computed LEAF-Q scores and BMI (r = -0.394; p < 0.01). A total of 42.9% of males (n = 18) and 68.6% of females (n = 35) were at risk for eating disorders, with females being at greater risk (p < 0.01). Body fat percentage was a predictor (ß = -0.095; p = -0.01) for eating disorder risk status. For every 1 unit increase in body fat percentage, athletes were 0.909 (95% CI: 0.845-0.977) times less likely to be classified as at risk for an eating disorder. Male (46.5 ± 13.9) and female (46.9 ± 11.4) athletes scored poorly on the ASNK-Q, with no differences between sex (p = 0.895). CONCLUSIONS: Female athletes were at a greater risk for eating disorders. No relationships existed between sport nutrition knowledge and %BF. Female athletes with a higher %BF had a lower risk for an eating disorder and risk for LEA.

Revisiting the Assessment of Strength, Power, and Change of Direction in Collegiate American Football Athletes.

ABSTRACT: Burke, AA, Guthrie, BM, Magee, M, Miller, AD, and Jones, MT. Revisiting the assessment of strength, power, and change of direction in collegiate american football athletes. J Strength Cond Res 37(8): 1623-1627, 2023-The primary purpose was to assess the effect of strength on vertical jump (VJ) and change of direction (COD) with a secondary purpose to examine if these relationships were moderated by the sport position group through path analysis using structural equation modeling. Subjects were collegiate American football athletes grouped by skill (SK; n = 98) and nonskill (NS; n = 83) sport position groups. Maximal strength was assessed by 1 repetition maximum back squat and hang clean (HC). Vertical jump was used to evaluate power. Change of direction was assessed through the 20-yard shuttle (i.e., 5-10-5) test. Multigroup path analysis examined causal pathways among variables and moderating effects of sport position. The final model revealed that VJ fully mediated the relationship between HC and COD (HC-VJ: ß = 0.408, p < 0.001; VJ-COD: ß = -0.376, p < 0.001; and HC-COD: ß = -0.137, p = 0.17) in SK. The NS showed only direct effects of HC on COD ( ß = -0.335, p < 0.001). These findings suggest that strength does not solely explain COD ability but also can serve to enhance underpinning qualities, such as relative force production in the VJ. Relationships between strength, power, and COD are different depending on the positional group. The results provide further insight into performance outcomes in field tests. It is recommended that sport position differences be considered when assessing and analyzing physical qualities in a team setting within the sport of American football.

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes.

Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution.Highlights All prediction equations significantly underestimated RMR in men athletes.All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes.Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations.In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations.

Contributing Factors to Low Energy Availability in Female Athletes: A Narrative Review of Energy Availability, Training Demands, Nutrition Barriers, Body Image, and Disordered Eating.

Relative Energy Deficiency in sport is experiencing remarkable popularity of late, particularly among female athletes. This condition is underpinned by low energy availability, which is a byproduct of high energy expenditure, inadequate energy intake, or a combination of the two. Several contributing factors exist that may predispose an athlete to low energy availability, and therefore a holistic and comprehensive assessment may be required to identify the root causes. The focus of the current narrative review is to discuss the primary contributing factors as well as known risk factors for low energy availability among female athletes to help practitioners increase awareness on the topic and identify future areas of focus.

The Influence of Sport Nutrition Knowledge on Body Composition and Perceptions of Dietary Requirements in Collegiate Athletes.

Sport nutrition knowledge has been shown to influence dietary habits of athletes. The purpose of the current study was to examine relationships between sport nutrition knowledge and body composition and examine potential predictors of body weight goals in collegiate athletes. Participants included National Collegiate Athletic Association Division III women (n = 42, height: 169.9 ± 6.9 cm; body mass: 67.1 ± 8.6 kg; fat-free mass: 51.3 ± 6.6 kg; body fat per cent: 24.2 ± 5.3%) and men (n = 25, height: 180.8 ± 7.2 cm; body mass: 89.2 ± 20.5 kg; fat-free mass: 75.9 ± 12.2 kg; body fat per cent: 13.5 ± 8.9%) athletes. Body composition was assessed via air displacement plethysmography. Athletes completed a validated questionnaire designed to assess sport nutrition knowledge and were asked questions about their perceived dietary energy and macronutrient requirements, as well as their body weight goal (i.e., lose, maintain, gain weight). Athletes answered 47.98 ± 11.29% of questions correctly on the nutrition questionnaire with no differences observed between sexes (men: 49.52 ± 11.76% vs. women: 47.03 ± 11.04%; p = 0.40). An inverse relationship between sport nutrition knowledge scores and body fat percentage (BF%) (r = -0.330; p = 0.008), and fat mass (r = -0.268; p = 0.032) was observed for all athletes. Fat mass (ß = 0.224), BF% (ß = 0.217), and body mass index (BMI) (ß = 0.421) were all significant (p < 0.05) predictors of body weight goal in women. All athletes significantly (p < 0.001) underestimated daily energy (-1360 ± 610.2 kcal/day), carbohydrate (-301.6 ± 149.2 grams/day [g/day]), and fat (-41.4 ± 34.5 g/day) requirements. Division III collegiate athletes have a low level of sport nutrition knowledge, which was associated with a higher BF%. Women athletes with a higher body weight, BF% and BMI were more likely to select weight loss as a body weight goal. Athletes also significantly underestimated their energy and carbohydrate requirements based upon the demands of their sport, independent of sex.

Does the Multistage 20-m Shuttle Run Test Accurately Predict VO2max in NCAA Division I Women Collegiate Field Hockey Athletes?

Laboratory assessments of maximal oxygen uptake (VO2max) are considered the "gold standard" for ascertaining cardiovascular fitness, but they are not always practical for use in team sport settings. Therefore, the purpose of the current study was to compare the criterion assessment of VO2max on a treadmill to the progressive, multistage 20-m shuttle run test (i.e., Beep test), and to determine the predictability of 6 previously established Beep test predictive equations (i.e., Chatterjee, Flouris, Leger, Leger and Gadoury, Ramsbottom, St. Clair-Gibson). Collegiate women field hockey athletes (n = 65, mean±SD: age 19.6 ± 1.2 years; weight 64.7 ± 6.1 kg) completed criterion VO2max (mean ± SD: 46.4 ± 4.6 mL·kg-1·min-1) and Beep tests to volitional fatigue. According to Bland-Altman and Ordinary Least Products Regressions, the Ramsbottom (46.5 ± 4.2 mL·kg-1·min-1) and Flouris (46.3 ± 3.8 mL·kg-1·min-1) equations were considered valid predictions of criterion measured VO2max (46.4 ± 4.6). The Chatterjee, Leger, Leger and Gadoury, and St. Clair-Gibson equations overestimated VO2max, and are not recommended for use with women collegiate field hockey athletes. The Ramsbottom and Flouris estimates of VO2max from 20-m shuttle performances may be used in this population. For accurate estimates of VO2max, the clientele's age, fitness level, and training history should be considered when selecting equations.

Prevalence of Low Energy Availability in Collegiate Women Soccer Athletes.

(1) Background: Limited information exists on the prevalence of low energy availability (LEA) in collegiate team sports. The purpose of this study was to examine the prevalence of LEA in collegiate women soccer players. (2) Methods: Collegiate women soccer athletes (n = 18, height: 1.67 ± 0.05 m; body mass: 65.3 ± 7.9 kg; body fat %: 24.9 ± 5.6%) had their body composition and sport nutrition knowledge assessed in the pre-season. Energy availability was assessed mid-season using a 4-day dietary log and activity energy expenditure values from a team-based monitoring system. A validated screening tool was used to screen for LEA. (3) Results: The screening tool classified 56.3% of athletes as at risk of LEA (<30 kcal/kg of FFM); however, the actual dietary intake identified 67% as LEA. Athletes identified as non-LEA consumed significantly more absolute (p = 0.040) and relative (p = 0.004) energy than LEA athletes. (4) Conclusions: There was a high prevalence of LEA among collegiate women soccer athletes. Although previously validated in women endurance athletes, the LEA screening tool was not effective in identifying those at risk of LEA in this sample of athletes.